Lead Score vs Ideal Customer Profile: A True Reflection

Lead Score vs Ideal Customer Profile: A True Reflection

Marketing & sales alignment is fragile. Sales pushes back on leads whose scores diverge with their intuition: “why am I getting assigned to a lead based in India. We never close deals in India. ”“Why is this lead scored low? We’re supposed to be going after accounts just like this. ”When sales pushes back on lead scoring, they lose confidence in the lead score. They stop using it to prioritize outreach, and don’t followup with good leads sent their way. Marketing feels frustrated about their work not being valued and they see increasing MQL disqualification and reduced conversion rates from MQL to Opportunity. Each side blames the other. As we've discussed this problem with some of the best marketing ops leaders in the software industry, a common source of disconnect was a fundamental misunderstanding of the relationship between Lead Scores & Ideal Customer Profile (ICP).  

Time & time again, marketing & sales teams expect that leads who score the highest should be the ones that most look like their ICP, and that's false.

Few teams have explicitly discussed this, so let’s dive in.

Defining your ICP & Lead Score

You’ve done your persona research. You know everything about Grace the Growth Guru, Frank the Finance Freak or Sheila the Sales Sherpa (persona researchers love alliteration). You know exactly the type of customers you want to go after, so you build out your ICP - company size, geography, industry, revenue, integrations - as a function of the type of customer you want to go after. Great. Your ICP will help guide you in your product roadmap - “What does Molly need?”, “How does this bring value to CompanyX?” - as well as your marketing & sales strategy. Your ICP is the goal. It’s where you want to go. It can and should be informed by the past (data), but it is a representation of where you want to go, not where you are. A Lead Score, meanwhile, is a quantifiable valuation of the quality of a lead. In early stage companies, it is often used to weed out spam and elevate big name VIPs to the top. As a company grows, the sales process complexities increase: tiered sales teams for self-serve vs. enterprise, geo-specific assignment, mixed inbound/outbound strategy & growth teams competing against both.

Any lead that looks identical to 100 leads that all turned into opportunities should be routed to sales and prioritized with a VIP treatment. Any lead that looks identical to 100 leads that stick to a free plan or have long sales cycles for low deal amounts should be ignored or prioritized as low importance. When Lead Score & ICP differ in opinion and why.

“This lead is garbage”

Intuition is a powerful thing, and often serves sales people well as they build relationships with prospects in order to help them solve a core problem; however, sales people interact with <1% of all leads and their sense of lead quality is often based on a single qualitative data point. When a lead is scored high that “doesn’t look good,” it usually comes down to a single data point. Last year we encountered a sales teams who wanted to override the score for leads based in India. They believed the market was not valuable to them, both in terms of available budget and operational costs. 10% of their new revenue in the previous quarter came from India. When they understood that, they asked that the country to be hidden from their sales team. There’s a lot to unpack here. Of course it’s not good to have a sweeping bias about an entire country, especially when it is to the detriment to your sales goals. For this company, we’re also not saying that all leads from India should get prioritization. We’re saying that they should prioritize 100 good leads from India, just like 100 good leads from anywhere should. Intuition is powerful, but Data doesn’t lie. Marketing has a responsibility not only to be data-driven, but to make the insights of that data available to all customer-facing teams. Modern marketing teams can enable modern sales teams not by providing them with 100 data points about every lead, but by providing a few key data points that explain why a lead gets scored the way it does. At MadKudu we call that Signals and it looks like this:

The combination of relevant firmographic & behavioral data points let's the sales team know why a lead scored a 92.

Inflection Points

Launching into new verticals & markets can present a real conundrum for your lead score. Historically, leads from, say, Japan, have not converted (because you weren’t targeting the Japanese market, weren’t compliant or a well-suited option), but this year you’re pushing into Japan and your upcoming campaign should bring in hundreds of new leads from Japan. You have expanded your ICP but your Lead Score is still measuring the likelihood of conversion based on historical data. The same problem arises as companies go up market, selling to increasingly large businesses. The added complexity here is that enterprise sales fundamentally looks different than velocity sales, so even if you’ve closed some enterprise clients in the past, your lead score may be heavily skewed towards velocity sales, making it hard to surface enterprise leads. You’ve expanded your ICP to include a new breed of business.

Overcoming Predictive Bias & Training your Model

ICP & Lead Scores digress at inflection points. Fast-growing businesses need to increasing existing market share at the same time as the seek to expand into new markets. Among leading go-to-market teams, we’ve observed two trends that make this combination possible at scale. Creating dedicated Business Development & Growth teams whose purpose is to bypass lead score and focusing on new market development areas, booking meetings directly for AEs. BDRs & Growth teams build up historical data over 3-6 months that can be used to retrain your lead score to account for the new market data.  Another method is to create hard exclusions to override your lead score. If your lead score is operationalized across the entire buyer journey, this is a quick way to experiment with new markets in an automatic way, but this should be rare. Hard exclusions are like a blindfold for predictive lead scores - you’re removing one of many signals from the equation, increasing the likelihood of false positives. As you make strategic changes to your business, they may be necessary for overcoming predictive bias.

Actionable Definitions

It is vital to have a common understanding across marketing & sales around what these tools are, how they are made, and how they should be used. While your Ideal Customer Profile paints a picture of who your vision will serve in the next year, your lead score needs to be the best way for sales to prioritize outreach. I’ve compiled a quick chart based on what we’ve seen from customers to illustrate the differences between ICP & Lead Score.  

This is something you can use to start a conversation at your next marketing & sales meeting about how Outbound Sales Strategy should be informed by your ICP or how you can increase forecast revenue for the next quarter based on the number of highly qualified leads you’re bringing in.